Parking charges have been widely accustomed in downtowns and commercial areas. Yet most of current practices of the parking prices are determined by the market itself, with limited consideration of the public interests, such as the overall performance of metropolitan transportation system, or their eco-social and environmental impacts.

In this research, we explore the idea of using integrated parking charges and carpooling incentives with consideration of the system total travel time and environmental costs, to manage daily commute traffic. We will analyze and compare various parking pricing and carpooling incentive policies under different scenarios at the metropolitan network level using a multi-layer, multi-scale system model. We will investigate how to properly select policies of parking charges and incentives to influence daily travel choices, and thus rebalance travel demand and enhance the system performance.

We’ll also integrate our parking/ridesharing model into the MTC’s Bay Area transportation model for the City of San Francisco, evaluate the impact of the proposed parking and carpool/ridesharing strategies on GHG and PM2.5 pollutant emissions.

The advent of shared mobility providers (e.g., Lyft, Uber, Bridj) has generated opportunities for the potential collaboration between transit agencies and these providers to explore the feasibility and benefits of complementary services.

This project will explore the potential service integration by developing and modeling a first and last mile transit access shared mobility service. In doing so the team will develop a hybrid model that combines the San Francisco Bay Area activity based travel demand model (MTC-ABM) with approximate routing and facility locations sub-routines to simulate the service. At the demonstrative level, the model will concentrate on access to subway systems for work and shopping related trips. The outcomes will be evaluated in terms of activity (VMT, congestion), and health impacts associated to the environmental emissions generated by the vehicles in the system. The team will differentiate between corridor and system level impacts.

The process method is three-fold: 1) the team will analyze the mode and destination choice models embedded in the MTC-ABM to identify and measure the sensitivity of choices decisions to various factors and variables. Using these results, the team will identify the network and behavioral factors that shed some light into the variables that that can produce a significant shift to other modes (i.e., shared mobility services); 2) develop approximation models considering districting and routing to simulate the movements of individuals from origins to pick-up locations and from drop-offs to destinations. The sub-routines models will provided updated parameters of approximate costs, and travel/transfer times that will be fed back into the MTC-ABM framework. And, 3) use the activity simulated results to estimate the health impacts of implementing such a complementary service.

The model could be expanded to evaluate different scenarios such as the use of zero or near zero emission vehicles as part of the shared mobility service; and benefits from autonomous and connected vehicles.

Considering shopping trips during the estimation of potential demand for the service will contribute to on-going research conducted by the authors to estimate the impacts of shopping versus residential deliveries.

We will use the Integrated Transport and Health Impacts Model (ITHIM) model developed by the California Department of Public Health to estimate health effects.